1,931
Views
46
CrossRef citations to date
0
Altmetric
Articles

Self-adaptive Multi-population Rao Algorithms for Engineering Design Optimization

&

References

  • Awad, N. H., M. Z. Ali, P. N. Suganthan, J. J. Liang, and B. Y. Qu. 2016. Problem definitions and evaluation criteria for the CEC 2017 special session and competition on single objective real-parameter numerical optimization. Technical report, Nanyang Technological University, Singapore.
  • Chowdhury, S., M. Marufuzzaman, H. Tunc, L. Bian, and W. Bullington. 2019. A modified ant colony optimization algorithm to solve a dynamic travelling salesman problem: A case study with drones for wildlife surveillance. Journal of Computational Design and Engineering 6:368–86. doi:10.1016/j.jcde.2018.10.004.
  • Coello, C. A. C. 2000. Treating constraints as objectives for single-objective evolutionary optimization. Engineering Optimization 32 (3):275–308. doi:10.1080/03052150008941301.
  • Derrac, J., S. Garcia, D. Molina, and F. Herrera. 2011. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation 1:3–18. doi:10.1016/j.swevo.2011.02.002.
  • Dorterler, M., I. Sahin, and H. Gokce. 2019. A grey wolf optimizer approach for optimal weight design problem of the spur gear. Engineering Optimization 51 (6):1013–27. doi:10.1080/0305215X.2018.1509963.
  • Gandomi, A. H., X. Yang, and A. H. Alavi. 2013. Cuckoo search algorithm: A metaheuristic approach to solve structural optimization problems. Engineering with Computers 29:17–35. doi:10.1007/s00366-011-0241-y.
  • Gu, L., R. J. Yang, C. H. Tho, M. Makowskit, O. Faruquet, and Y. Lit. 2001. Optimization and robustness for crashworthiness of side impact. International Journal of Vehicle Design 26 (4):348–60. doi:10.1504/IJVD.2001.005210.
  • Gulcu, S., and H. Kodaz. 2015. A novel parallel multi-swarm algorithm based on comprehensive learning particle swarm optimization. Engineering Applications of Artificial Intelligence 45:33–45. doi:10.1016/j.engappai.2015.06.013.
  • Hashim, F. A., E. H. Houssein, M. S. Mabrouk, A. Walid, and S. Mirjalili. 2019. Henry gas solubility optimization: A novel physics-based algorithm. Future Generation Computer Systems 101:646–67. doi:10.1016/j.future.2019.07.015.
  • Jena, P. K., D. N. Thatoi, and D. R. Parhi. 2015. Dynamically self-adaptive fuzzy PSO technique for smart diagnosis of transverse crack. Applied Artificial Intelligence 29 (3):211–32. doi:10.1080/08839514.2015.1004611.
  • Li, C., T. T. Nguyen, M. Yang, S. Yang, and S. Zeng. 2015. Multi-population methods in un-constrained continuous dynamic environments: The challenges. Information Sciences 296:95–118. doi:10.1016/j.ins.2014.10.062.
  • Li, C., and S. Yang. 2008. Fast multi-swarm optimization for dynamic optimization problems. In Proceedings of the Fourth International Conference on Natural Computation, ICNC’08, 7, Jinan, China, IEEE, 624–28.  doi:10.1109/ICNC.2008.313
  • Mirjalili, S. 2015a. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-based Systems 89:228–49. doi:10.1016/j.knosys.2015.07.006.
  • Mirjalili, S. 2015b. The ant lion optimizer. Advances in Engineering Software 83:80–98. doi:10.1016/j.advengsoft.2015.01.010.
  • Mirjalili, S., and A. Lewis. 2016. The whale optimization algorithm. Advances in Engineering Software 95:51–67. doi:10.1016/j.advengsoft.2016.01.008.
  • Mirjalili, S., and S. M. Mirjalili. 2016. Multi-verse optimizer: A nature-inspired algorithm for global optimization. Neural Computing and Applications 27 (2):495–513. doi:10.1007/s00521-015-1870-7.
  • Mirjalili, S., S. M. Mirjalili, and A. Lewis. 2014. Grey wolf optimizer. Advances in Engineering Software 69:46–61. doi:10.1016/j.advengsoft.2013.12.007.
  • Mortazavi, A. 2019. Interactive fuzzy search algorithm: A new self-adaptive hybrid optimization algorithm. Engineering Applications of Artificial Intelligence 81:270–82. doi:10.1016/j.engappai.2019.03.005.
  • Nseef, S. K., S. Abdullah, A. Turky, and G. Kendall. 2016. An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems. Knowledge-based Systems 104:14–23. doi:10.1016/j.knosys.2016.04.005.
  • Rao, R. V. 2016. Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations 7 (1):19–34.
  • Rao, R. V. 2019. Jaya: An advanced optimization algorithm and its engineering applications. Switzerland: Springer International Publishing.
  • Rao, R. V. 2020. Rao algorithms: Three metaphor-less simple algorithms for solving optimization problems. International Journal of Industrial Engineering Computations 11:107–30. doi:10.5267/j.ijiec.2019.6.002.
  • Rao, R. V., and V. K. Patel. 2013. Multi-objective optimization of heat exchangers using modified teaching-learning–Based optimization. Applied Mathematical Modelling 37 (3):1147–62. doi:10.1016/j.apm.2012.03.043.
  • Rao, R. V., and A. Saroj. 2017. A self-adaptive multi-population based Jaya algorithm for engineering optimization. Swarm and Evolutionary Computation 37:1–26. doi:10.1016/j.swevo.2017.04.008.
  • Rao, R. V., V. J. Savsani, and D. P. Vakharia. 2011. Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-Aided Design 43 (3):303–15. doi:10.1016/j.cad.2010.12.015.
  • Rizk-Allah, R. M. 2018. Hybridizing sine cosine algorithm with multi-orthogonal search strategy for engineering design problems. Journal of Computational Design and Engineering 5:249–73. doi:10.1016/j.jcde.2017.08.002.
  • Savsani, V., R. V. Rao, and D. P. Vakharia. 2010. Optimal weight design of a gear train using particle swarm optimization and simulated annealing algorithms. Mechanism and Machine Theory 45:531–41. doi:10.1016/j.mechmachtheory.2009.10.010.
  • Turky, A. M., and S. Abdullah. 2014. A multi-population harmony search algorithm with external archive for dynamic optimization problems. Information Sciences 272 (1):84–95. doi:10.1016/j.ins.2014.02.084.
  • Vafashoar, R., and M. R. Meybodi. 2018. Multi swarm optimization algorithm with adaptive connectivity degree. Applied Intelligence 48:909–41. doi:10.1007/s10489-017-1039-4.
  • Wang, S., Y. Li, and H. Yang. 2017. Self-adaptive differential evolution algorithm with improved mutation mode. Applied Intelligence 47:644–58. doi:10.1007/s10489-017-0914-3.
  • Xia, L., J. Chu, and Z. Geng. 2014. A multiswarm competitive particle swarm algorithm for optimization control of an ethylene cracking furnace. Applied Artificial Intelligence 28 (1):30–46. doi:10.1080/08839514.2014.862772.
  • Yang, S., and C. Li. 2010. A clustering particle swarm optimizer for locating and tracking multiple optima in dynamic environments. IEEE Transactions on Evolutionary Computation 14 (6):959–74. doi:10.1109/TEVC.2010.2046667.
  • Yildiz, A. R., H. Abderazek, and S. Mirjalili. 2019. A comparative study of recent non-traditional methods for mechanical design optimization. Archives of Computational Methods in Engineering 1–18. doi:10.1007/s11831-019-09343-x
  • Yokota, T., T. Taguchi, and M. Gen. 1998. A solution method for optimal weight design problem of the gear using genetic algorithms. Computers and Industrial Engineering 35 (3–4):523–26. doi:10.1016/S0360-8352(98)00149-1.
  • Zarchi, D. A., and B. Vahidi. 2018. Multi objective self adaptive optimization method to maximize ampacity and minimize cost of underground cables. Journal of Computational Design and Engineering 5:401–08. doi:10.1016/j.jcde.2018.02.004.
  • Zhang, J., and X. Ding. 2011. A multi-swarm self-adaptive and cooperative particle swarm optimization. Engineering Applications of Artificial Intelligence 24:958–67. doi:10.1016/j.engappai.2011.05.010.
  • Zhao, Z., B. Liu, C. Zhang, and H. Liu. 2019. An improved adaptive NSGA-II with multi-population algorithm. Applied Intelligence 49:569–80. doi:10.1007/s10489-018-1263-6.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.